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SYSTEMATIC REVIEW article

Front. Med.
Sec. Infectious Diseases: Pathogenesis and Therapy
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1492709

Mapping Knowledge Landscapes and Emerging Trends in Artificial Intelligence for Antimicrobial Resistance: Bibliometric and Visualization Analysis

Provisionally accepted
Zhongli Wang Zhongli Wang 1,2Gaopei Zhu Gaopei Zhu 3Shixue Li Shixue Li 1,2*
  • 1 Center for Health Management and Policy Research, Shandong University, Jinan, Shandong Province, China
  • 2 Key Laboratory of Health Economy and Policy Research, Shandong University, Jinan, Shandong Province, China
  • 3 School of Public Health, Shandong University, Jinan, Shandong Province, China

The final, formatted version of the article will be published soon.

    Objective: To systematically map the knowledge landscape and development trends in artificial intelligence (AI) applications for antimicrobial resistance (AMR) research through bibliometric analysis, providing evidence-based insights to guide future research directions and inform strategic decision-making in this dynamic field. Methods: A comprehensive bibliometric analysis was performed using the Web of Science Core Collection database for publications from 2014 to 2024. The analysis integrated multiple bibliometric approaches: VOSviewer for visualization of collaboration networks and research clusters, CiteSpace for temporal evolution analysis, and quantitative analysis of publication metrics. Key bibliometric indicators including co-authorship patterns, keyword co-occurrence, and citation impact were analyzed to delineate research evolution and collaboration patterns in this domain. Results: A collection of 2,408 publications was analyzed, demonstrating significant annual growth with publications increasing from 4 in 2014 to 549 in 2023 (22.7% of total output). The United States (707), China (581), and India (233) were the leading contributors in international collaborations. The Chinese Academy of Sciences (53), Harvard Medical School (43), and University of California San Diego (26) were identified as top contributing institutions. Citation analysis highlighted two major breakthroughs: AlphaFold's protein structure prediction (6,811 citations) and deep learning approaches to antibiotic discovery (4,784 citations). Keyword analysis identified six enduring research clusters from 2014 to 2024: sepsis, artificial neural networks, antimicrobial resistance, antimicrobial peptides, drug repurposing, and molecular docking, demonstrating the sustained integration of AI in antimicrobial therapy development. Recent trends show increasing application of AI technologies in traditional approaches, particularly in MALDI-TOF MS for pathogen identification and graph neural networks for large-scale molecular screening. Conclusion: This bibliometric analysis shows the importance of artificial intelligence in enhancing the progress in the discovery of antimicrobial drugs especially towards the fight against AMR. From enhancing the fast, efficient and predictive performance of drug discovery methods, current AI capabilities have revealed observable potential to be proactive in combating the ever-growing challenge of AMR worldwide. This study serves not only an identification of current trends, but also, and especially, offers a strategic approach to further investigations.

    Keywords: artificial intelligence, antimicrobial resistance, antibiotics, bibliometric analysis, deep learning

    Received: 07 Sep 2024; Accepted: 13 Jan 2025.

    Copyright: © 2025 Wang, Zhu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Shixue Li, Center for Health Management and Policy Research, Shandong University, Jinan, Shandong Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.